Pretrained language models have achieved state-of-the-art performance when adapted to a downstream NLP task. However, theoretical analysis of these models is scarce and challenging since the pretraining and downstream tasks can be very different.

For the experiments described in Section 3.5 of the main paper, all methods which required a Bayesian These residuals are obtained by first training a separate classifier per each class, and then calculating the residual as the error between the predicted and ground truth class. Training Hyperparameters For each method, we used a batch size of 128 and a learning rate of 0.001. Each method was trained until convergence for 200 epochs. To validate that our "non-noisy" class conditioning approach is RBCC, and the class ordering implies that each class is predicted before its parent classes. Results are shown in Figure 1.

Recurrent Classifier Chains (RCCs), a recurrent neural network extension of ensemble-based classifier chains, are the state-of-the-art exact multi-label classification method for maximizing subset accuracy.

Discrimination in machine learning often arises along multiple dimensions (a.k.a.